An Approach to Aggregate Intuitionistic Fuzzy Information with the Help of Linear Operator

Abstract

In real-world scenario, the information about the process is incomplete and imprecise. The Atanassov Intuitionistic Fuzzy Set (AIFS) is a powerful and flexible tool to handle such uncertainty in a system efficiently. The first two components of AIFS, namely, membership part and non-membership part, model the behavior of the features/criteria which describe the process. So corresponding to each feature, selection of linear/nonlinear function as per the requirement of the process is made. The third component of AIFS, called hesitancy part which describes the uncertainty in the system. In this paper, we define a new linear aggregation operator for AIFS known as, Intuitionistic Fuzzy Linear Aggregation Operator (IFLWA). Further, a numerical example is given to demonstrate the procedure of implementing IFLWA in Technique for order Preference by Similarity to ideal Solution (TOPSIS) for solving supply chain management problem. The results derived through IFLWA shows that it has the capability to handle the TOPSIS method in an efficient manner.